Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems.

中文翻译：

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用于人员重新识别的操作员在环深度顺序多摄像头特征融合


给定目标图像作为查询，人员重新识别系统会基于每个摄像机检索候选匹配的排名列表。在部署的系统中，操作员扫描这些列表并通过触摸或基于鼠标的选择来标记所看到的目标。然而，经典的重新识别方法独立生成每个摄像机的列表。因此，操作员在摄像机子集中进行的目标识别不能用于提高剩余网络摄像机组中目标的排名。为了解决这个缺点，我们提出了一种新颖的顺序多摄像头重识别方法。所提出的方法可以适应人类操作员的输入，并通过目标排名的单调改进提供早期收益。我们方法的核心是一个融合函数，它对查询和候选匹配的深层特征表示进行操作。我们制定了定制设计的优化程序，以逐步改进查询表示。由于现有的评估方法不能直接应用于我们的设置，我们还提出了两种新颖的评估协议。两个大型 re-id 数据集（Market-1501、DukeMTMC-reID）上的结果表明，我们的多摄像头方法显着优于基线和其他流行的特征融合方案。此外，我们还对人类操作员的表现进行了基于主题的比较研究。我们的方法所实现的卓越操作员性能为其集成到可部署视频监控系统中提供了令人信服的理由。